Vehicle systems, such as autonomous driving, advanced driver-assist systems (ADAS), often need highly accurate positioning information to operate correctly. Global Navigation Satellite Systems (GNSS), such as Global positioning system (GPS) and/or similar satellite-based positioning technologies can provide such positioning data in open sky scenarios. However, the performance of GPS drastically degrades if large parts of the sky are obstructed. This occurs, for example, in so-called “urban canyon” scenarios, where GPS-based estimated positions may be off by as much as 50 m. These large positioning errors can be prohibitive in vehicular automation and/or navigation.
A main cause for these large positioning errors in GPS-based positioning is measurements from non-line-of-sight (NLoS) satellites. These are satellites for which the direct line-of-sight (LoS) path from the satellite to a GNSS/GPS receiver at the vehicle is blocked, and the receiver instead erroneously tracks a NLoS signal, which may be reflected off a building or other object. Since these reflected NLoS signals can travel a path up to hundreds of meters longer, they can cause significant positioning errors. Detecting and excluding such NLoS satellites from positioning or location determinations may therefore be necessary for good positioning performance in urban scenarios. Vision-aided positioning techniques use camera information to detect these NLoS satellites. However, current vision-aided positioning techniques for determining these NLoS satellites can be costly, often requiring one or more cameras on top of the vehicle.